Learning Dense Representations of Phrases at Scale
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Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019). However, current phrase retrieval models heavily depend on their sparse representations while still underperforming retriever-reader approaches. In this work, we show for the first time that we can learn dense phrase representations alone that achieve much stronger performance in open-domain QA. Our approach includes (1) learning query-agnostic phrase representations via question generation and distillation; (2) novel negative-sampling methods for global normalization; (3) query-side fine-tuning for transfer learning. On five popular QA datasets, our model DensePhrases improves previous phrase retrieval models by 15 matches the performance of state-of-the-art retriever-reader models. Our model is easy to parallelize due to pure dense representations and processes more than 10 questions per second on CPUs. Finally, we directly use our pre-indexed dense phrase representations for two slot filling tasks, showing the promise of utilizing DensePhrases as a dense knowledge base for downstream tasks.
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